## Replication Materials: _Misinformation Beyond Traditional Feeds: Evidence from a WhatsApp Deactivation Experiment in Brazil_

Replication materials for *Misinformation Beyond Traditional Feeds: Evidence from a WhatsApp Deactivation Experiment in Brazil* authored by Tiago Ventura, Rajeshwari Majumdar, Jonathan Nagler and Joshua A. Tucker, and conditionally accepted at the Journal of Politics

> __Abstract:__
In most advanced democracies, concerns about the spread of misinformation are typically associated with feed-based social media platforms like Twitter and Facebook. These platforms also account for the vast majority of research on the topic. However, in most of the world, particularly in Global South countries, misinformation often reaches citizens through social media messaging apps, particularly WhatsApp. To fill the resulting gap in the literature, we conducted a multimedia deactivation experiment to test the impact of reducing exposure to potential sources of misinformation on WhatsApp during the weeks leading up to the 2022 Presidential election in Brazil. We find that this intervention significantly reduced participants’ recall of false rumors circulating widely during the election. However, consistent with theories of mass media minimal effects, a short-term change in the information environment did not lead to significant changes in belief accuracy, political polarization, or well-being.
> 

## Tutorial

This README file provides an overview of the replication materials for the article.

The R code used in the article and online supplemental materials can be found in the folder `**code**`.

The datasets for the full replication of the article and online supplemental materials can be found in the folder `**data**`.

All figures and tables in the article and online supplemental materials are available in the folder `**output**`.

We also provide a [codebook](codebook.pdf) describing all variables contained in raw dataset.

## Code

- `_utils.R`: this contains all of the user-defined functions required for data cleaning, analysis, and presentation. 

- `00_process_data.R`: this code performs data wrangling tasks with the raw experimental data. It produces `all_processed_data.rds`, which is used in the analysis of the paper.

- `01_analysis_compliance.R`: this code is for compliance descriptive analysis. It generates Figure 2 in the main article and Figure 4 in the supplemental materials.

- `02_analysis_primary_outcomes.R`: this code runs results for primary outcomes discussed in the paper. It produces Figures 3–6 in the main article. This code also produces Tables 17, 18, and 19 (multiple hypothesis adjustments) in the supplemental materials.

- `03_analysis_sm_additional_results.R`: this code generates results for additional analyses presented in the supplemental materials. It produces Figures 5, 6, 7, 8, 9, 10, 11, 12, 15, and 19 and Tables 9, 13, and 14 in the supplemental materials.

- `04_balance_table.R`: this code is for balance tables and attrition analysis. It produces Tables 3-8 in the supplemental materials.

- `05_heterogenous_effects_tables.R`: this code produces tables assessing heterogeneous effects. It produces Tables 10, 11, and 12 in the supplemental materials.

- `06_analysis_descriptive_whatsapp_usage.R`: this code provides descriptive information about WhatsApp usage during the experiment. It produces Figures 16, 17, and 18 in the supplemental materials.

- `07_bayes_factor.R`: this code produces the Bayes factor results presented in the supplemental materials (Table 20).

- `08_power_analysis.R`: this code replicates the pre-registered power analysis and the post-hoc power analysis in the supplemental materials (Figures 13 and 14)

- `09_analysis_iv_table16.R`: this code produces the Table 16 in the supplemental materials. 

## Data

See the codebook for information about the variables in the datasets below.

- `all_raw_data.rds`: this dataset combines all pre-treatment and post-treatment survey outcomes in raw format (post-anonymization, i.e., removing contact information).

- `all_processed_data.rds`: this dataset combines all pre-treatment and post-treatment survey outcomes after some pre-processing. This dataset is generated by running the code `00_process_data.R`.

- `pre_treatment.rds`: this dataset contains only pre-treatment variables in the format used in scripts that produce balance and attrition tables. This dataset has participants that started but did not fully enrolled in the experiment. 

- `compliance.csv`: this dataset contains information about compliance for every participant. The data is fully anonymized.

### Additional datasets

In addition to our experimental data, we also incorporate in the paper information from additional datasets based on the Brazilian Electoral Survey 2022 (BES), and an online sample of WhatsApp users collected by the Interdisciplinary Lab for Computational Social Science (iLCSS) at the University of Maryland, College Park. These datasets are available in the data folder.

- `eseb_quotas.csv`: quotas from the BES survey

- `04810.sav`: microdata from the BES survey

- `brazil_2022.csv`: iLCSS online data

## Computational Infrastructure

To ensure reproducibility, here is the output of `sessionInfo()` at the time this project was last run:

```r
session_info()
```

```
R version 4.3.1 
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.5.1

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] compiler_4.3.1    fastmap_1.1.1     rsconnect_1.1.0   cli_3.6.4         htmltools_0.5.7   tools_4.3.1      
 [7] rstudioapi_0.15.0 yaml_2.3.9        rmarkdown_2.25    knitr_1.45        xfun_0.47         digest_0.6.33    
[13] lifecycle_1.0.4   rlang_1.1.5       evaluate_0.23    

```
